This course familiarizes you with various aspects of conducting research with online consumer behavior (e.g., click stream) and social media data (e.g., Twitter). Specifically, you will study state-of-the-art literature, learn emerging programming and statistical methods, and discuss insights during lectures and computer lab sessions.|
At the end of this course, you will be able to:
A recent course book with all necessary details is available on the course's Blackboard site.
- Explain the structure of databases and handle the first steps in retrieving and managing (incl. merging and aggregating) large datasets.
- Write basic code in important programming languages (e.g., SQL, Python).
- Conduct statistical analyses on large datasets from companies such as Twitter, Spotify and Amazon.com.
- Translate the results of analyses and the theoretical insights gained from studying the literature into managerially relevant findings.
- Critically evaluate own contribution as well as the contribution of others.
- Discuss used methods, results of analyses and relevant academic literature in writing and/or orally.
The enrollment procedure for this course is different for different groups:
All students who registered correctly will be enrolled in the OSIRIS course by program management, after which they will be automatically added to the Blackboard page of the course. If there are any questions or problems, please contact TiSEM-MSc-Marketing-Management-Research@uvt.nl immediately.
- Students in MSc Marketing Analytics can self-enroll from January 14 until February 15 (noon).
- Students in MSc Marketing Management February 2019 entrants need to enroll via the procedure published on the MSc Marketing Management General Information Blackboard page before February 15 (noon).
- Students in MSc Marketing Management of earlier entry moments need to send a message to TiSEM-MSc-Marketing-Management-Research@uvt.nl before February 15 (noon).
- Students in Research Master need to send a message to TiSEM-MSc-Marketing-Management-Research@uvt.nl before February 15 (noon).
This course is not open to student from other programs.
The content is divided into four blocks, consisting of
- Data I
- Database technology: Data retrieval, management, and analysis using SQL
- Data II
- Data science skills in Python
- Web scraping and APIs (for data retrieval and for artificial intelligence)
- Methods I
- User-generated content on social and digital media
- Conducting sentiment analysis in Python
- Methods II
- Display advertising and search engine advertising
- Evaluating large-scale online (quasi) field experiments
Typically, students should have the following characteristics when following this class:
- Strong interest in (learning about) statistical analyses and data science; albeit no profound knowledge is required beforehand (except a course in Marketing Research)
- Willingness to learn about a diverse set of computer programs, and readiness to learn writing computer code (e.g., Python code)
- Willingness to engage into self-studying (e.g., via web lectures)
- Willingness to work in teams, and being evaluated by other team members
- Willingness to work hard
Type of instructions
Lectures, computer lab sessions, self-study (e.g., using web clips)
Type of exams
Quizzes and team assignments (during the course of the semester, in total 40%), and computer exam (60%). A total course grade equal to 5.5 or higher is required to pass the course.
- The syllabus for the course will be made available at the start of the course.
- There is no need to purchase books in advance.